The analyst industrial complex

If you've never worked in analyst relations, here's the short version: large technology companies pay research firms (Gartner, Forrester, IDC) to evaluate their products. The analysts write reports. The reports influence enterprise buyers. The companies that manage their analyst relationships well end up in favourable quadrants and waves. The ones that don't, struggle to close enterprise deals.

It's a multi-billion pound industry built on relationships, reputation, and an astonishing amount of manual work.

The manual machine

A typical AR programme at a mid-to-large tech company involves tracking dozens of analysts across multiple firms. Each analyst covers a specific market segment. Each has their own publication schedule, research methodology, and opinion landscape. The AR team's job is to know all of this, maintain relationships with each analyst, and ensure the company is positioned correctly in every relevant report.

The tools they use? Spreadsheets. Calendar invites. Internal wikis that haven't been updated since 2023. Maybe a CRM that was repurposed from sales. The institutional knowledge lives in people's heads, and when the AR lead leaves, most of it walks out with them.

The institutional knowledge lives in people's heads. When the AR lead leaves, most of it walks out with them.

What AI sees that humans miss

An AI system that continuously monitors analyst publications, social media, conference appearances, and research agendas can build a real-time map of the analyst landscape that no human team could maintain. It can spot shifts in coverage before they become trends. It can identify which analysts are gaining influence in emerging categories. It can flag when a competitor has increased their analyst engagement cadence.

More importantly, it can synthesise. When an analyst publishes a new framework, an AI system can immediately map your product's capabilities against it and identify the gaps. When you're preparing for an analyst briefing, it can pull together everything that analyst has published in the last six months, highlight their current concerns, and suggest talking points that address them.

The bigger opportunity

The really interesting question isn't "can we make AR more efficient?" It's "can we democratise it?" Today, only companies with large budgets can afford dedicated AR teams. That means the analyst landscape — which heavily influences enterprise purchasing — is shaped by a small number of voices, mostly from the largest vendors.

AI-powered AR tooling could change that. If a 50-person startup can manage analyst relationships as effectively as a 5,000-person enterprise, the analyst ecosystem becomes more competitive, more diverse, and arguably more useful for the buyers who depend on it.

This is the thesis we're exploring with Pipeline #3. We don't know yet whether the economics work. We don't know whether analyst firms would embrace or resist AI-powered engagement. We don't know whether the market is ready. That's what the research phase is for.

Not every sidequest makes it. That's the point.